Dynamic Warp Scheduling for Improved GPU Utilization

Modern GPUs execute thousands of threads organized into warps or wavefronts. The order in which these groups are scheduled significantly impacts performance, but current schedulers use simple policies that don't adapt to workload characteristics.

1. The Warp Scheduling Challenge

GPU performance depends heavily on hiding memory latency through massive parallelism. When a warp stalls on memory access, the scheduler must choose which ready warp to execute next.

1.1 Current Scheduling Policies

Round-Robin (RR)

• Simple, fair scheduling
• No consideration of warp characteristics
• Poor performance for mixed workloads

Greedy-Then-Oldest (GTO)

• Prioritize warps that recently made progress
• Better cache locality
• Can starve some warps

Loose Round-Robin (LRR)

• Hybrid approach: RR with some locality
• Moderate performance across workloads
• Still doesn't adapt to workload characteristics

2. The Machine Learning Approach

This paper introduces AdaptiveWarp, an ML-based scheduler that learns optimal policies in real-time.

2.1 Workload Characterization

The system characterizes workloads using hardware performance counters:

Memory Characteristics:

• Cache hit rates (L1, L2, texture)
• Memory bandwidth utilization
• Average memory latency
• Coalescing efficiency

Compute Characteristics:

• Arithmetic intensity (ops/byte)
• Branch divergence frequency
• Register pressure
• Occupancy levels

Temporal Patterns:

• Phase behavior detection
• Memory access patterns
• Synchronization frequency

2.2 Neural Network Architecture

Network Details:

• Input: 47 features from performance counters
• Hidden layers: 64 and 32 neurons with ReLU activation
• Output: 4 scheduling policies (RR, GTO, LRR, Custom)
• Training: Online reinforcement learning with reward based on IPC

2.3 Hardware Implementation

Inference Engine:

• 2KB on-chip neural network accelerator
• 8-bit quantized weights
• 10-cycle inference latency
• Negligible power overhead (less than 0.5%)

Performance Counter Integration:

• Existing GPU performance monitoring units
• Additional counters for memory coalescing
• Warp-level statistics tracking

3. Experimental Evaluation

3.1 Methodology

• Simulated on GPGPU-Sim with modern GPU architecture model
• 28 diverse benchmarks from Rodinia, PolyBench, and CUDA SDK
• Compared against RR, GTO, LRR baselines

3.2 Performance Results

Overall Performance Gains:

• Average: 18.2% IPC improvement over best static policy
• Memory-bound workloads: 25.3% improvement
• Compute-bound workloads: 12.1% improvement
• Mixed workloads: 21.7% improvement

Per-Benchmark Analysis:

• Best case (Hotspot): 34% improvement
• Worst case (Matrix Multiply): 3% improvement
• Geometric mean: 16.8% across all benchmarks

3.3 Energy Efficiency

• Performance per watt: 17.1% improvement
• ML inference overhead: 0.3% of total GPU power
• Net energy savings: 14.2% due to reduced execution time

3.4 Adaptability Analysis

The scheduler successfully adapts to different phases:

Phase Detection:

• Identifies memory-intensive phases → prioritizes cache-friendly scheduling
• Detects compute-intensive phases → focuses on maximizing throughput
• Recognizes synchronization points → optimizes for fairness

Dynamic Switching:

• Average policy switch frequency: 2.3 times per kernel
• Switch overhead: less than 1% performance impact
• Convergence time: 50-100 cycles after phase change

4. Real-World Implications

4.1 Industry Adoption Potential

High-Performance GPU Integration:

• Compatible with existing streaming multiprocessor architectures
• Minimal changes to warp scheduler logic
• Could be deployed in future datacenter GPU generations

Consumer GPU Integration:

• Applicable to modern wavefront scheduling architectures
• Similar performance counter infrastructure exists
• Potential for next-generation consumer GPU integration

4.2 Software Ecosystem Impact

CUDA/HIP Programming:

• Transparent to existing applications
• No source code changes required
• Automatic optimization for diverse workloads

Compiler Optimizations:

• Reduced need for manual scheduling hints
• More robust performance across different inputs
• Simplified performance tuning process

5. Technical Challenges

5.1 Training Data Collection

• Cold start problem: Initial performance before learning
• Solution: Hybrid approach with static fallback policies
• Convergence: 95% of optimal performance within 1000 cycles

5.2 Hardware Constraints

• Area overhead: 0.8% of SM area for inference engine
• Power overhead: 0.5% of total GPU power consumption
• Latency: Must complete inference within scheduling window

5.3 Workload Diversity

• Generalization: Trained on subset, tested on unseen workloads
• Robustness: 92% of performance gains maintained on new benchmarks
• Adaptation: Continues learning during deployment

6. Future Research Directions

6.1 Advanced ML Techniques

• Transformer architectures: Better sequence modeling for temporal patterns
• Federated learning: Share knowledge across different GPU deployments
• Reinforcement learning: More sophisticated reward functions

6.2 Extended Scope

• Multi-GPU scheduling: Coordinate across multiple devices
• CPU-GPU heterogeneous: Joint scheduling decisions
• Memory hierarchy: Include memory controller scheduling

6.3 Security Considerations

• Side-channel attacks: ML models could leak information
• Adversarial workloads: Malicious code exploiting scheduler
• Isolation: Ensure scheduling doesn't break security boundaries

7. Critical Assessment

Strengths:

• Significant performance improvements across diverse workloads
• Practical hardware implementation with low overhead
• Comprehensive evaluation methodology
• Real-world applicability

Limitations:

• Limited evaluation on newer GPU architectures
• No analysis of multi-tenant scenarios
• Security implications not thoroughly explored
• Training overhead not fully characterized

8. Key Takeaways

1. Static scheduling policies are suboptimal for diverse GPU workloads
2. Machine learning can effectively adapt to workload characteristics in real-time
3. Hardware implementation is feasible with minimal overhead
4. Performance gains are substantial (15-25%) across different workload types
5. Industry adoption is likely given the clear benefits and low implementation cost

This research demonstrates that intelligent, adaptive scheduling can significantly improve GPU utilization, paving the way for more efficient parallel computing systems.